This invention relates to a process for dynamic monitoring of changes to deformable media, and for predicting these changes.
In domains such as air traffic control, it is important not only to monitor trajectories followed by aircraft to guide them, but also to know the weather conditions within the radius of action of the instruments used for this control, and particularly for detecting and precisely localizing cloud formations that could be dangerous or simply unpleasant for persons in the aircraft. This is usually done using weather radars, for which the echo images are displayed on the control center screen after eliminating echoes due to fixed obstacles. Human interpretation of these types of image is often complicated and these images cannot be used to forecast changes to cloud formations. Furthermore, an air traffic controller needs to know the weather conditions and how they will change in order to optimize routing of aircraft that he is responsible for guiding and to understand any route changes decided upon by the captain to avoid cloud formations that he considers are dangerous. The article by F. BARBARESCO, S. BONNEY, J. LAMBERT and B. MONNIER published in IEEE International Conference on Image Processingxe2x80x94I.C.I.P. 96, in Lausanne (Switzerland) in September 1996 entitled xe2x80x9cMotion Based Segmentation and Tracking of Dynamic Radar Clutterxe2x80x9d describes a process for processing this type of image by determining active contours with a constraint model, but this process has two disadvantages; firstly it requires a prohibitive calculation time, and secondly it cannot manage complex deformations (it simply manages deformations that can be approximated by an affine deformation model).
The purpose of this invention is a process for dynamically monitoring the variation of deformable media that can be used for this monitoring and for creating relatively reliable forecasts of how the deformations will change, without requiring a prohibitive calculation time.
The process according to the invention for dynamic monitoring of deformable media consists of using at least two images taken at different times obtained by at least one sensor, establishing the skeleton of each distinct assembly in this medium for each image, and then making the image skeletons in question correspond to each other.
This correspondence is made by vectorizing the skeletons of the processed images, examining the vectors of these skeletons taken in pairs, and each time attempting to match two vectors with approximately the same geometric characteristics. Skeletons of assemblies, and if required the corresponding assemblies, can be rebuilt from these matched skeletons.
Advantageously, the image may be preprocessed before the skeletons are built up. This preprocessing may include a filter step (frequency and/or morphological filtering), and a thresholding step eliminating image components that are not relevant for the problem in question. The processing can advantageously be facilitated by simplifying the skeletons by vectorization (thinning and/or search for points at which curves representative of skeletons have a curvature greater than a determined value and then linearization) and/or elimination of insignificant artifacts or barbules.
According to one aspect of the process according to the invention, a map of displacement vectors is created for at least one area of interest in the processed images.
According to another aspect of the invention, a predicted image is built up starting from displacement vectors and/or the skeleton. Another possible way of rebuilding the above mentioned image is to use the skeleton displacement vectors to calculate the displacement of each pixel image and to rebuild the predicted image from the newly predicted pixel locations.
According to another aspect of the process according to the invention, attributes are applied to at least some of the pixels in several images taken at different times, the characteristics of the corresponding areas are determined, and information about the variation and/or the nature of areas is thus enriched. These attributes are particularly the change in the area or volume of deformable media, their density, and the local variation of density within these media.